114 Testing a Successive Correction Based Data Assimilation Methodology in the NWS Hydrology Laboratory Research Distributed Hydrological Model

Tuesday, 12 January 2016
Robert J. Zamora, NOAA/ESRL, Boulder, CO; and A. R. Thorstensen and R. Cifelli


Testing a successive correction based data assimilation methodology in the NWS Hydrology Laboratory Research Distributed Hydrological Model

Robert Zamora1;  Andrea Thorstensen2; Rob Cifelli1

1NOAA Earth System Research Laboratory, Physical Sciences Division, Boulder, Colorado, USA

2 Department of Civil and Environmental Engineering, University of California, Irvine, California, USA

The NOAA ESRL Physical Sciences Division (NOAA/ESRL/PSD) in collaboration with the California Department of Water Resources has deployed a network of soil moisture observing stations in the Russian River basin. The basin encompasses 1,485 square miles within Sonoma and Mendocino Counties, California. NOAA/ERSL/PSD has also implemented the NOAA National Weather Service Office of Hydrologic Development Research Distributed Hydrological Model (HL-RDHM) in the basin.

The soil moisture observations have been used to evaluate the performance of the Sacramento Model Heat Transfer (SAC-HT) soil moisture parameterization used by HL-RDHM.  The intercomparison between the NOAA/ESRL/PSD observations and the simulated soil moisture fields shows that model performance in the basin varies seasonally. The best model skill was found during late winter and spring. Poorer performance was found during late fall and early winter.

Statistical analysis of both the observations and the HL-RDHM simulations suggests that data assimilation strategies can be used to improve the late fall and early winter performance of the HL-RDHM soil moisture parameterization. In this presentation we will show the results of the intercomparision, and the statistical analyses. In addition, the design and testing of a simple successive-correction soil moisture data assimilation scheme will be presented. Successive-correction data assimilation methods are computationally efficient alternatives to 4-D Variational and Ensemble Kalman filtering methods. 


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